NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
In the last 50 years there has been a tremendous progress in solving numerically the Navier-
Stokes equations using finite differences, finite elements, spectral, and even meshless …
Stokes equations using finite differences, finite elements, spectral, and even meshless …
Convolutional-network models to predict wall-bounded turbulence from wall quantities
Two models based on convolutional neural networks are trained to predict the two-
dimensional instantaneous velocity-fluctuation fields at different wall-normal locations in a …
dimensional instantaneous velocity-fluctuation fields at different wall-normal locations in a …
[HTML][HTML] A predictive hybrid reduced order model based on proper orthogonal decomposition combined with deep learning architectures
R Abadía-Heredia, M López-Martín, B Carro… - Expert Systems with …, 2022 - Elsevier
Solving computational fluid dynamics problems requires using large computational
resources. The computational time and memory requirements to solve realistic problems …
resources. The computational time and memory requirements to solve realistic problems …
Flow reconstruction from sparse sensors based on reduced-order autoencoder state estimation
The reconstruction of accurate and robust unsteady flow fields from sparse and noisy data in
real-life engineering tasks is challenging, particularly when sensors are randomly placed. To …
real-life engineering tasks is challenging, particularly when sensors are randomly placed. To …
[图书][B] Data-driven fluid mechanics: combining first principles and machine learning
Data-driven methods have become an essential part of the methodological portfolio of fluid
dynamicists, motivating students and practitioners to gather practical knowledge from a …
dynamicists, motivating students and practitioners to gather practical knowledge from a …
Time-resolved reconstruction of flow field around a circular cylinder by recurrent neural networks based on non-time-resolved particle image velocimetry …
Particle image velocimetry (PIV) has been extensively used in wind-tunnel test for flow-field
measurement. However, the sampling frequency of traditional PIV is low and physics of flow …
measurement. However, the sampling frequency of traditional PIV is low and physics of flow …
物理增强的流场深度学习建模与模拟方法
金晓威， 李惠 - 力学学报, 2021 - lxxb.cstam.org.cn
流体运动理论上可用Navier− Stokes 方程描述, 但由于对流项带来的非线性,
仅在少数情况可求得方程解析解. 对于复杂工程流动问题, 数值模拟难以高效精准计算高雷诺数 …
仅在少数情况可求得方程解析解. 对于复杂工程流动问题, 数值模拟难以高效精准计算高雷诺数 …
Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements
Different types of neural networks have been used to solve the flow sensing problem in
turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements …
turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements …
A deep learning framework for reconstructing experimental missing flow field of hydrofoil
Hydrofoils play a crucial role in enhancing the efficiency of fluid machinery designed for
ocean environments, reducing lift-induced drag and contributing to improved overall …
ocean environments, reducing lift-induced drag and contributing to improved overall …
Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental data
Recent work has demonstrated the use of sparse sensors in combination with the proper
orthogonal decomposition (POD) to produce data-driven reconstructions of the full velocity …
orthogonal decomposition (POD) to produce data-driven reconstructions of the full velocity …